Space Science with Python - Cassini #7: Bayesian Blocks & Project Conclusion
Repository: https://github.com/ThomasAlbin/Astron...
Today we conclude our first Cassini side-project by conducting an ML experiment on single dimensional data to compute a regression function (velocity vs. rise time). In this video we will use Bayesian Blocks for a un-biased sample weighting and a custom loss function to "enforce" certain functional behaviour.
Credit - Thumbnail: NASA/JPL-Caltech
---
Content
0:00 Introduction
1:12 Coding - Read in the data
1:50 Coding - Prior Calibrations
2:10 Coding - "Original data view"
3:20 Coding - Bayesian Blocks - Distribution
6:24 Coding - Bayesian-based weighting
7:20 Coding - Machine Learning - Custom Loss Function
10:17 Coding - Machine Learning - Hypermodel
14:25 Coding - Best ML Model
14:56 Coding - Plotting the final model
11:20 Summary & Outlook
---
How to setup a local dev environment: • Space Science with Python - Part 2: Setup ...
---
There is a lot to do and to learn and I hope you will join the journey. Meanwhile, if you have questions or ideas, reach out to me via:
Mastodon: https://astrodon.social/@ThomasAlbin
Twitter: / mrastrothomas
Reddit: / mrastrothomas
GitHub: https://github.com/ThomasAlbin
Or drop a comment!
Talk to you later,
Thomas
#space #science #Python #tutorial #datascience #cassini #nasa #saturn #ml #ai #machinelearning #deeplearning